MODULE LEAD - Data Management/Mining/Collection

GCP Data Engineer (68 Years Experience) Role Overview We are looking for a highly skilled Data Engineer with 68 years of experience to design, build, and optimize scalable data pipelines and AI-driven data solutions. The ideal candidate will have hands-on expertise in Kubernetes (GKE) , GCP ecosystem , RAG-based architectures , and agentic/AI pipelines , along with experience in data processing, extraction, and integration workflows. Key Responsibilities Design and develop scalable data pipelines for data extraction, transformation, and ingestion. Build and manage AI/ML data pipelines, including RAG (Retrieval-Augmented Generation) and agent-based workflows. Develop and orchestrate data extraction and injection pipelines across structured and unstructured data sources. Deploy and manage application jobs on Kubernetes (GKE) for high availability and performance. Integrate with GCP services such as BigQuery, Cloud Storage, Pub/Sub, Dataflow, and Apigee. Work with SDP (Sensitive Data Protection/DLP) and Model Armor for secure data handling and compliance. Collaborate with cross-functional teams to ensure data governance, security, and privacy compliance. Build and maintain CI/CD pipelines using GitHub and related DevOps tools. Optimize performance, scalability, and cost-efficiency of data and AI systems. Troubleshoot production issues and ensure system reliability. Required Skills & Experience Core Technical Skills Strong experience in Kubernetes and Google Kubernetes Engine (GKE) Hands-on experience with Google Cloud Platform (GCP) services Expertise in data pipeline development (batch & real-time) Experience with data extraction, transformation, and ingestion pipelines Solid understanding of RAG architectures and vector-based retrieval systems Experience in building agentic pipelines / AI-driven workflows Platform & Tools GitHub (version control, CI/CD workflows) Apigee (API management and integration) SDP/DLP and Model Armor (data security and compliance handling) Experience with event-driven architectures (Pub/Sub, Eventarc preferred) Programming & Data Strong programming skills in Python / Pyspark Experience working with SQL and NoSQL databases Familiarity with BigQuery, Databricks, or Spark-based processing Good to Have Experience with Vertex AI / AI platform integration Knowledge of data masking, tokenization, and privacy frameworks Exposure to healthcare or sensitive data ecosystems (PII/PHI handling) Understanding of microservices architecture and API-driven systems Data Management/Mining/Collection